Повторная идентификация людей в системах видеонаблюдения с использованием глубокого обучения: анализ существующих методов

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Аннотация

Статья посвящена многостороннему анализу повторной идентификации людей в системах видеонаблюдения и современных методов ее решения с использованием глубокого обучения. Рассматриваются общие принципы и применение сверточных нейронных сетей для этой задачи. Предложена классификация систем реидентификации. Приведен анализ существующих наборов данных для обучения глубоких нейронных архитектур, описаны подходы для увеличения количества изображений в базах данных. Рассматриваются подходы к формированию признаков изображений людей. Представлен анализ основных применяемых для реидентификации моделей архитектур сверточных нейронных сетей, их модификаций, а также методов обучения. Анализируется эффективность повторной идентификации на разных наборах данных, приведены результаты исследований по оценке эффективности существующих подходов в различных метриках.

Об авторах

Х. Чен

Чжэцзян Шурен университет

Email: eric.hf.chen@hotman.com
Ханчжоу

С. А Игнатьева

Полоцкий государственный университет имени Евфросинии Полоцкой

Email: s.ignatieva@psu.by
Новополоцк

Р. П Богуш

Полоцкий государственный университет имени Евфросинии Полоцкой

Email: r.bogush@psu.by
Новополоцк

С. В Абламейко

Белорусский государственный университет

Автор, ответственный за переписку.
Email: ablameyko@bsu.by
Минск

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